REGR_INTERCEPT
REGR_INTERCEPT(Y, X)
Section titled “REGR_INTERCEPT(Y, X)”- y: This is the dependent variable in the regression equation. It denotes the response or the output value that the equation predicts.
- x: This is the independent variable in the regression equation. It denotes the predictor or the input value that the equation uses to forecast the dependent variable Y.
Example
Section titled “Example”SELECT REGR_INTERCEPT(y, x) as interceptFROM (SELECT 1 as x, 3 as y FROM dual UNION ALL SELECT 2 as x, 5 as y FROM dual UNION ALL SELECT 3 as x, 7 as y FROM dual)Output
Section titled “Output”INTERCEPT-----------1Explanation
Section titled “Explanation”The REGR_INTERCEPT function computes the y-intercept of the least-squares-fit linear equation determined by the (x, y) pairs. In the code above, (1, 3), (2, 5), and (3, 7) pairs are provided. These points all lie on the line y = 2x + 1, hence intercept of the line which is the expected output returns 1.
REGR_INTERCEPT(Y, X)
Section titled “REGR_INTERCEPT(Y, X)”- y: The dependent variable in the linear regression model. This variable’s values are predicted from the independent variable. Y can be any numerical type that PostgreSQL recognizes, including integers, floating point numbers, and decimals.
- x: The independent variable in the linear regression model. Changes in this variable’s values are believed to explain changes in the dependent variable. Similar to Y, X can also be any numerical type recognized by PostgreSQL.
Example
Section titled “Example”CREATE TABLE data (x float, y float);INSERT INTO data VALUES (1, 3), (2, 5), (3, 7), (4, 9);SELECT REGR_INTERCEPT(y, x) FROM data;Output
Section titled “Output” regr_intercept---------------- 1Explanation
Section titled “Explanation”In this example, the function REGR_INTERCEPT(y, x) is used to compute the y-intercept of the least-squares regression line fitted to the points (x, y). The resulting output shows that the line crosses the y-axis at 1.